Application/app.py

import streamlit as st
import pandas as pd
import joblib
from huggingface_hub import hf_hub_download

from modelConnector import ModelConnector

# ===========================
#  LOAD MODEL & DATASET
# ===========================

st.title("📊 Is Click Predictor")

# Download and load the trained model from Hugging Face
model_path = hf_hub_download(repo_id="taimax13/is_click_predictor", filename="rf_model.pkl")
rf_model = joblib.load(model_path)
st.success("✅ Model Loaded Successfully!")

# ===========================
#  LOAD DATA FROM HUGGING FACE
# ===========================

st.sidebar.header("Dataset Selection")

# # Download required dataset files
# X_test_path = hf_hub_download(repo_id="taimax13/is_click_data", filename="X_test_1st(1).csv")
# y_test_path = hf_hub_download(repo_id="taimax13/is_click_data", filename="y_test_1st.csv")
# train_data_path = hf_hub_download(repo_id="taimax13/is_click_data", filename="train_dataset_full - train_dataset_full (1).csv")

X_test_path = "HuggingFaceRepo/data/y_test_1st (1).csv"
y_test_path = "HuggingFaceRepo/data/y_test_1st.csv"
train_data_path = "HuggingFaceRepo/data/train_dataset_full - train_dataset_full.csv"


# Load datasets
X_test = pd.read_csv(X_test_path)
y_test = pd.read_csv(y_test_path, header=None)  # Ensure labels match test dataset index
train_data = pd.read_csv(train_data_path)

st.info(f"✅ Loaded datasets: **Train: {len(train_data)} rows**, **Test: {len(X_test)} rows**")



# Initialize Model Connector
model_connector = ModelConnector()

st.title("📊 Is Click Predictor - Train, Retrain, and Predict")

# ===========================
#  CHECK MODEL STATUS
# ===========================

if model_connector.model:
    st.success("✅ Model Loaded Successfully!")
else:
    st.warning("⚠ No model found. Please train one first.")

# ===========================
#  TRAIN MODEL IF NOT FOUND
# ===========================

if st.button("🚀 Train Model"):
    st.info("🔄 Training model...")
    message = model_connector.train_model()
    st.success(message)

# ===========================
#  RETRAIN MODEL
# ===========================

if st.button("🔄 Retrain Model"):
    st.info("🔄 Retraining model with latest data...")
    message = model_connector.retrain_model()
    st.success(message)


# ===========================
#  SELECT A DATA SAMPLE
# ===========================

st.sidebar.header("Select a Test Sample for Prediction")

# Merge X_test with y_test for selection (without labels affecting prediction)
X_test["actual_click"] = y_test.values

# Allow user to pick a row
selected_index = st.sidebar.selectbox("Choose a test sample index", X_test.index)
selected_row = X_test.loc[selected_index].drop("actual_click")  # Exclude actual label

# Display selected row
st.write("### Selected Data Sample:")
st.dataframe(selected_row.to_frame().T)  # Display as a table



# ===========================
#  MAKE PREDICTION & EXPORT CSV
# ===========================

if st.button("Predict Click"):
    # Convert selected row to DataFrame for model input
    input_data = selected_row.to_frame().T

    # Make prediction
    prediction = rf_model.predict(input_data)[0]

    # Add prediction to DataFrame
    input_data["is_click_predicted"] = prediction

    # Save prediction as CSV
    csv_filename = "prediction_result.csv"
    input_data.to_csv(csv_filename, index=False)

    # Display Prediction Result
    st.subheader("Prediction Result")
    if prediction == 1:
        st.success("🟢 The model predicts: **User WILL CLICK on the ad!**")
    else:
        st.warning("🔴 The model predicts: **User WILL NOT CLICK on the ad.**")

    # Provide download button for prediction result
    st.download_button(
        label="📥 Download Prediction Result",
        data=input_data.to_csv(index=False).encode("utf-8"),
        file_name="prediction_result.csv",
        mime="text/csv",
    )

st.markdown("---")
st.info("Select a test row from the **left panel**, click **'Predict Click'**, and download the prediction result as a CSV.")